naranjja

sudo su 
curl https://packages.microsoft.com/keys/microsoft.asc | apt-key add -
curl https://packages.microsoft.com/config/ubuntu/18.04/prod.list > /etc/apt/sources.list.d/mssql-release.list
sudo apt-get update
sudo ACCEPT_EULA=Y apt-get install msodbcsql17
sudo ACCEPT_EULA=Y apt-get install mssql-tools
echo 'export PATH="$PATH:/opt/mssql-tools/bin"' >> ~/.bash_profile
echo 'export PATH="$PATH:/opt/mssql-tools/bin"' >> ~/.bashrc
source ~/.bashrc
sudo apt-get install unixodbc unixodbc-dev

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TRACKING_ID="UA-XXXXXXXX-Y"

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import pandas as pd

def explode_array(df, column_to_explode, axis=1, pop=False, keep=None):
    new_cols = []
    if axis == 0:
        rows = np.repeat(range(df.shape[0]), df[column_to_explode].apply(len))
        cols = [i for i, _ in enumerate(df.columns) if _ != column_to_explode]
        new_df = df.iloc[rows, cols].copy()
        new_df[column_to_explode] = [_ for row in df[column_to_explode] for _ in row]
        new_cols.append(column_to_explode)

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# look for package
dpkg --get-selections | grep '<PACKAGE_NAME_SUBSTRING>'

# remove package
sudo apt-get --purge remove <FOUND_PACKAGE_NAME>

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sudo nano ~/.bashrc

# add a line like this:
alias example='sudo apt-get update'

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gsutil -m cp -n * gs://path/to/folder
# -m: parallel job for multiple files
# -n: do not overwrite

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def validate(passphrase):
    passphrase = passphrase.split(' ')
    for word in passphrase:
        if passphrase.count(word) > 1: return 0
    return 1

def f(x):
    y = 0
    for passphrase in x:
        y += validate(passphrase)

naranjja

SVG tags are in another namespace

• /g becomes /*[name()="g"]
• /svg becomes /*[name()="svg"]
• /path becomes /*[name()="path"]

For example, using Selenium:

driver.find_element_by_xpath("//ul/li/svg")  # wrong
driver.find_element_by_xpath("./ul/li//*[name()='svg']" # right

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adb exec-out screencap -p > ~/screenshots/filename.png

# with timestamp filename
adb exec-out screencap -p > ~/screenshots/$(date +%s).png

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import pandas as pd
import numpy as np

def drop_outliers(df, field_name):
    distance = 1.5 * (np.nanpercentile(df[field_name], 75) - np.nanpercentile(df[field_name], 25))
    df.drop(df[df[field_name] > distance + np.nanpercentile(df[field_name], 75)].index, inplace=True)
    df.drop(df[df[field_name] < np.nanpercentile(df[field_name], 25) - distance].index, inplace=True)

if __name__ == "__main__":
  # assuming df exists and contains numeric variables